Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 6; pp. 7783 - 7795
• Main Authors: Chai, Furong, Zhang, Qi, Yao, Haipeng, Xin, Xiangjun, Gao, Ran, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; attention mechanism; Computation offloading; Computer architecture; Costs; Edge computing; Graph theory; Internet of Things; Mobile computing; mobile edge computing (MEC); Multi-task offloading; Multitasking; Optimization; proximal policy optimization (PPO); Resource allocation; Resource management; Resource scheduling; Satellite communications; Satellite Internet of Things (IoT); Satellites; Task analysis; Task scheduling; Unmanned aerial vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3238771

For multi-task mobile edge computing (MEC) systems in satellite Internet of Things (IoT), there are dependencies between different tasks, which need to be collected and jointly offloaded. It is crucial to allocate the computing and communication resources reasonably due to the scarcity of satellite communication and computing resources. To address this issue, we propose a joint multi-task offloading and resource allocation scheme in satellite IoT to improve the offloading efficiency. We first construct a novel resource allocation and task scheduling system in which tasks are collected and decided by multiple unmanned aerial vehicles (UAV) based aerial base stations, the edge computing services are provided by satellites. Furthermore, we investigate the multi-task joint computation offloading problem in the framework. Specifically, we model tasks with dependencies as directed acyclic graphs (DAG), then we propose an attention mechanism and proximal policy optimization (A-PPO) collaborative algorithm to learn the best offloading strategy. The simulation results show that the A-PPO algorithm can converge in 25 steps. Furthermore, the A-PPO algorithm reduces cost by at least 8.87<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> compared to several baseline algorithms. In summary, this paper provides a new insight for the cost optimization of multi-task MEC systems in satellite IoT.